Overview

Dataset statistics

Number of variables44
Number of observations71980
Missing cells424146
Missing cells (%)13.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory42.5 MiB
Average record size in memory619.5 B

Variable types

Numeric24
Categorical19
Boolean1

Warnings

merchant_category has a high cardinality: 56 distinct values High cardinality
merchant_group has a high cardinality: 12 distinct values High cardinality
uuid has a high cardinality: 71980 distinct values High cardinality
account_amount_added_12_24m is highly correlated with sum_capital_paid_account_0_12m and 1 other fieldsHigh correlation
account_days_in_rem_12_24m is highly correlated with account_worst_status_12_24mHigh correlation
account_days_in_term_12_24m is highly correlated with account_worst_status_12_24mHigh correlation
account_worst_status_0_3m is highly correlated with account_worst_status_3_6mHigh correlation
account_worst_status_12_24m is highly correlated with account_days_in_rem_12_24m and 1 other fieldsHigh correlation
account_worst_status_3_6m is highly correlated with account_worst_status_0_3mHigh correlation
avg_payment_span_0_12m is highly correlated with avg_payment_span_0_3m and 3 other fieldsHigh correlation
avg_payment_span_0_3m is highly correlated with avg_payment_span_0_12m and 1 other fieldsHigh correlation
has_paid is highly correlated with status_last_archived_0_24m and 3 other fieldsHigh correlation
max_paid_inv_0_12m is highly correlated with max_paid_inv_0_24m and 1 other fieldsHigh correlation
max_paid_inv_0_24m is highly correlated with max_paid_inv_0_12mHigh correlation
num_active_inv is highly correlated with num_arch_ok_0_12m and 2 other fieldsHigh correlation
num_arch_ok_0_12m is highly correlated with num_active_inv and 2 other fieldsHigh correlation
num_arch_ok_12_24m is highly correlated with num_active_inv and 2 other fieldsHigh correlation
num_arch_rem_0_12m is highly correlated with status_max_archived_0_12_monthsHigh correlation
status_last_archived_0_24m is highly correlated with avg_payment_span_0_12m and 6 other fieldsHigh correlation
status_2nd_last_archived_0_24m is highly correlated with has_paid and 5 other fieldsHigh correlation
status_3rd_last_archived_0_24m is highly correlated with status_2nd_last_archived_0_24m and 3 other fieldsHigh correlation
status_max_archived_0_6_months is highly correlated with status_last_archived_0_24m and 4 other fieldsHigh correlation
status_max_archived_0_12_months is highly correlated with avg_payment_span_0_12m and 7 other fieldsHigh correlation
status_max_archived_0_24_months is highly correlated with avg_payment_span_0_12m and 6 other fieldsHigh correlation
sum_capital_paid_account_0_12m is highly correlated with account_amount_added_12_24m and 1 other fieldsHigh correlation
sum_capital_paid_account_12_24m is highly correlated with account_amount_added_12_24m and 1 other fieldsHigh correlation
sum_paid_inv_0_12m is highly correlated with max_paid_inv_0_12m and 3 other fieldsHigh correlation
account_amount_added_12_24m is highly correlated with num_unpaid_bills and 2 other fieldsHigh correlation
account_days_in_rem_12_24m is highly correlated with account_worst_status_12_24m and 1 other fieldsHigh correlation
account_incoming_debt_vs_paid_0_24m is highly correlated with num_unpaid_billsHigh correlation
account_worst_status_0_3m is highly correlated with account_worst_status_3_6mHigh correlation
account_worst_status_12_24m is highly correlated with account_days_in_rem_12_24mHigh correlation
account_worst_status_3_6m is highly correlated with account_worst_status_0_3mHigh correlation
avg_payment_span_0_12m is highly correlated with avg_payment_span_0_3m and 3 other fieldsHigh correlation
avg_payment_span_0_3m is highly correlated with avg_payment_span_0_12mHigh correlation
has_paid is highly correlated with max_paid_inv_0_12m and 9 other fieldsHigh correlation
max_paid_inv_0_12m is highly correlated with has_paid and 9 other fieldsHigh correlation
max_paid_inv_0_24m is highly correlated with has_paid and 10 other fieldsHigh correlation
num_active_div_by_paid_inv_0_12m is highly correlated with num_active_inv and 1 other fieldsHigh correlation
num_active_inv is highly correlated with num_active_div_by_paid_inv_0_12m and 1 other fieldsHigh correlation
num_arch_ok_0_12m is highly correlated with has_paid and 8 other fieldsHigh correlation
num_arch_ok_12_24m is highly correlated with max_paid_inv_0_24m and 4 other fieldsHigh correlation
num_arch_rem_0_12m is highly correlated with avg_payment_span_0_12m and 3 other fieldsHigh correlation
num_unpaid_bills is highly correlated with account_amount_added_12_24m and 5 other fieldsHigh correlation
status_last_archived_0_24m is highly correlated with has_paid and 8 other fieldsHigh correlation
status_2nd_last_archived_0_24m is highly correlated with has_paid and 10 other fieldsHigh correlation
status_3rd_last_archived_0_24m is highly correlated with has_paid and 10 other fieldsHigh correlation
status_max_archived_0_6_months is highly correlated with has_paid and 10 other fieldsHigh correlation
status_max_archived_0_12_months is highly correlated with avg_payment_span_0_12m and 11 other fieldsHigh correlation
status_max_archived_0_24_months is highly correlated with avg_payment_span_0_12m and 10 other fieldsHigh correlation
sum_capital_paid_account_0_12m is highly correlated with account_amount_added_12_24m and 2 other fieldsHigh correlation
sum_capital_paid_account_12_24m is highly correlated with account_amount_added_12_24m and 3 other fieldsHigh correlation
sum_paid_inv_0_12m is highly correlated with has_paid and 10 other fieldsHigh correlation
account_amount_added_12_24m is highly correlated with sum_capital_paid_account_0_12m and 1 other fieldsHigh correlation
account_days_in_rem_12_24m is highly correlated with account_worst_status_12_24mHigh correlation
account_worst_status_12_24m is highly correlated with account_days_in_rem_12_24mHigh correlation
avg_payment_span_0_12m is highly correlated with avg_payment_span_0_3m and 1 other fieldsHigh correlation
avg_payment_span_0_3m is highly correlated with avg_payment_span_0_12mHigh correlation
has_paid is highly correlated with status_last_archived_0_24m and 4 other fieldsHigh correlation
max_paid_inv_0_12m is highly correlated with max_paid_inv_0_24m and 1 other fieldsHigh correlation
max_paid_inv_0_24m is highly correlated with max_paid_inv_0_12m and 1 other fieldsHigh correlation
num_active_div_by_paid_inv_0_12m is highly correlated with num_active_inv and 1 other fieldsHigh correlation
num_active_inv is highly correlated with num_active_div_by_paid_inv_0_12m and 1 other fieldsHigh correlation
num_arch_ok_0_12m is highly correlated with num_arch_ok_12_24m and 3 other fieldsHigh correlation
num_arch_ok_12_24m is highly correlated with num_arch_ok_0_12m and 2 other fieldsHigh correlation
num_arch_rem_0_12m is highly correlated with status_max_archived_0_12_months and 1 other fieldsHigh correlation
num_unpaid_bills is highly correlated with num_active_div_by_paid_inv_0_12m and 2 other fieldsHigh correlation
status_last_archived_0_24m is highly correlated with has_paid and 5 other fieldsHigh correlation
status_2nd_last_archived_0_24m is highly correlated with has_paid and 5 other fieldsHigh correlation
status_3rd_last_archived_0_24m is highly correlated with has_paid and 8 other fieldsHigh correlation
status_max_archived_0_6_months is highly correlated with num_arch_ok_0_12m and 6 other fieldsHigh correlation
status_max_archived_0_12_months is highly correlated with avg_payment_span_0_12m and 8 other fieldsHigh correlation
status_max_archived_0_24_months is highly correlated with has_paid and 6 other fieldsHigh correlation
sum_capital_paid_account_0_12m is highly correlated with account_amount_added_12_24m and 2 other fieldsHigh correlation
sum_capital_paid_account_12_24m is highly correlated with account_amount_added_12_24m and 1 other fieldsHigh correlation
sum_paid_inv_0_12m is highly correlated with max_paid_inv_0_12m and 6 other fieldsHigh correlation
account_worst_status_12_24m is highly correlated with account_worst_status_6_12m and 3 other fieldsHigh correlation
account_worst_status_3_6m is highly correlated with account_worst_status_6_12m and 1 other fieldsHigh correlation
worst_status_active_inv is highly correlated with recovery_debtHigh correlation
status_last_archived_0_24m is highly correlated with status_max_archived_0_24_months and 6 other fieldsHigh correlation
max_paid_inv_0_12m is highly correlated with max_paid_inv_0_24mHigh correlation
account_worst_status_6_12m is highly correlated with account_worst_status_12_24m and 2 other fieldsHigh correlation
max_paid_inv_0_24m is highly correlated with max_paid_inv_0_12mHigh correlation
num_arch_ok_0_12m is highly correlated with num_active_inv and 2 other fieldsHigh correlation
status_max_archived_0_24_months is highly correlated with status_last_archived_0_24m and 8 other fieldsHigh correlation
num_arch_rem_0_12m is highly correlated with sum_paid_inv_0_12mHigh correlation
status_max_archived_0_12_months is highly correlated with status_last_archived_0_24m and 8 other fieldsHigh correlation
num_arch_written_off_0_12m is highly correlated with status_max_archived_0_24_months and 1 other fieldsHigh correlation
num_arch_dc_0_12m is highly correlated with num_arch_dc_12_24mHigh correlation
sum_capital_paid_account_0_12m is highly correlated with sum_capital_paid_account_12_24mHigh correlation
avg_payment_span_0_12m is highly correlated with status_last_archived_0_24m and 5 other fieldsHigh correlation
avg_payment_span_0_3m is highly correlated with status_last_archived_0_24m and 3 other fieldsHigh correlation
account_days_in_rem_12_24m is highly correlated with account_worst_status_12_24mHigh correlation
status_3rd_last_archived_0_24m is highly correlated with status_max_archived_0_24_months and 4 other fieldsHigh correlation
num_arch_written_off_12_24m is highly correlated with status_max_archived_0_24_months and 1 other fieldsHigh correlation
merchant_group is highly correlated with merchant_categoryHigh correlation
num_arch_dc_12_24m is highly correlated with num_arch_dc_0_12mHigh correlation
status_2nd_last_archived_0_24m is highly correlated with status_last_archived_0_24m and 7 other fieldsHigh correlation
num_unpaid_bills is highly correlated with num_active_inv and 1 other fieldsHigh correlation
account_days_in_term_12_24m is highly correlated with account_worst_status_12_24mHigh correlation
num_active_inv is highly correlated with num_arch_ok_0_12m and 3 other fieldsHigh correlation
sum_paid_inv_0_12m is highly correlated with num_arch_ok_0_12m and 4 other fieldsHigh correlation
num_arch_ok_12_24m is highly correlated with num_arch_ok_0_12m and 2 other fieldsHigh correlation
recovery_debt is highly correlated with worst_status_active_inv and 2 other fieldsHigh correlation
account_worst_status_0_3m is highly correlated with account_worst_status_3_6m and 1 other fieldsHigh correlation
merchant_category is highly correlated with merchant_groupHigh correlation
account_days_in_dc_12_24m is highly correlated with account_worst_status_12_24mHigh correlation
sum_capital_paid_account_12_24m is highly correlated with sum_capital_paid_account_0_12mHigh correlation
status_max_archived_0_6_months is highly correlated with status_last_archived_0_24m and 6 other fieldsHigh correlation
has_paid is highly correlated with status_last_archived_0_24m and 4 other fieldsHigh correlation
status_last_archived_0_24m is highly correlated with status_max_archived_0_24_months and 3 other fieldsHigh correlation
status_max_archived_0_24_months is highly correlated with status_last_archived_0_24m and 6 other fieldsHigh correlation
status_max_archived_0_12_months is highly correlated with status_last_archived_0_24m and 5 other fieldsHigh correlation
num_arch_written_off_0_12m is highly correlated with status_max_archived_0_24_months and 2 other fieldsHigh correlation
status_3rd_last_archived_0_24m is highly correlated with status_max_archived_0_12_months and 2 other fieldsHigh correlation
num_arch_written_off_12_24m is highly correlated with status_max_archived_0_24_monthsHigh correlation
merchant_group is highly correlated with merchant_categoryHigh correlation
status_2nd_last_archived_0_24m is highly correlated with status_max_archived_0_24_months and 1 other fieldsHigh correlation
merchant_category is highly correlated with merchant_groupHigh correlation
status_max_archived_0_6_months is highly correlated with status_last_archived_0_24m and 3 other fieldsHigh correlation
has_paid is highly correlated with status_last_archived_0_24m and 5 other fieldsHigh correlation
account_days_in_dc_12_24m has 8540 (11.9%) missing values Missing
account_days_in_rem_12_24m has 8540 (11.9%) missing values Missing
account_days_in_term_12_24m has 8540 (11.9%) missing values Missing
account_incoming_debt_vs_paid_0_24m has 42629 (59.2%) missing values Missing
account_status has 39100 (54.3%) missing values Missing
account_worst_status_0_3m has 39100 (54.3%) missing values Missing
account_worst_status_12_24m has 48018 (66.7%) missing values Missing
account_worst_status_3_6m has 41500 (57.7%) missing values Missing
account_worst_status_6_12m has 43415 (60.3%) missing values Missing
avg_payment_span_0_12m has 17086 (23.7%) missing values Missing
avg_payment_span_0_3m has 35356 (49.1%) missing values Missing
num_active_div_by_paid_inv_0_12m has 16438 (22.8%) missing values Missing
num_arch_written_off_0_12m has 12957 (18.0%) missing values Missing
num_arch_written_off_12_24m has 12957 (18.0%) missing values Missing
worst_status_active_inv has 49970 (69.4%) missing values Missing
account_days_in_dc_12_24m is highly skewed (γ1 = 37.44849558) Skewed
account_incoming_debt_vs_paid_0_24m is highly skewed (γ1 = 111.6008925) Skewed
recovery_debt is highly skewed (γ1 = 51.15070937) Skewed
row_id is uniformly distributed Uniform
uuid is uniformly distributed Uniform
row_id has unique values Unique
uuid has unique values Unique
account_amount_added_12_24m has 51292 (71.3%) zeros Zeros
account_days_in_dc_12_24m has 63242 (87.9%) zeros Zeros
account_days_in_rem_12_24m has 56481 (78.5%) zeros Zeros
account_days_in_term_12_24m has 62547 (86.9%) zeros Zeros
account_incoming_debt_vs_paid_0_24m has 9460 (13.1%) zeros Zeros
max_paid_inv_0_12m has 15535 (21.6%) zeros Zeros
max_paid_inv_0_24m has 12626 (17.5%) zeros Zeros
num_active_div_by_paid_inv_0_12m has 35071 (48.7%) zeros Zeros
num_active_inv has 49970 (69.4%) zeros Zeros
num_arch_dc_0_12m has 68934 (95.8%) zeros Zeros
num_arch_dc_12_24m has 69104 (96.0%) zeros Zeros
num_arch_ok_0_12m has 19662 (27.3%) zeros Zeros
num_arch_ok_12_24m has 27188 (37.8%) zeros Zeros
num_arch_rem_0_12m has 55234 (76.7%) zeros Zeros
num_unpaid_bills has 37379 (51.9%) zeros Zeros
recovery_debt has 71821 (99.8%) zeros Zeros
sum_capital_paid_account_0_12m has 47492 (66.0%) zeros Zeros
sum_capital_paid_account_12_24m has 53768 (74.7%) zeros Zeros
sum_paid_inv_0_12m has 15535 (21.6%) zeros Zeros

Reproduction

Analysis started2021-05-19 03:27:57.785528
Analysis finished2021-05-19 03:29:40.449306
Duration1 minute and 42.66 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

account_amount_added_12_24m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct17948
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12270.56421
Minimum0
Maximum1128775
Zeros51292
Zeros (%)71.3%
Negative0
Negative (%)0.0%
Memory size562.5 KiB
2021-05-19T00:29:40.541334image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35063
95-th percentile73077.8
Maximum1128775
Range1128775
Interquartile range (IQR)5063

Descriptive statistics

Standard deviation35343.59459
Coefficient of variation (CV)2.880356109
Kurtosis89.10906279
Mean12270.56421
Median Absolute Deviation (MAD)0
Skewness6.654912253
Sum883235212
Variance1249169679
MonotonicityNot monotonic
2021-05-19T00:29:40.670965image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
051292
71.3%
3026
 
< 0.1%
5022
 
< 0.1%
6017
 
< 0.1%
9017
 
< 0.1%
10016
 
< 0.1%
809
 
< 0.1%
406
 
< 0.1%
205
 
< 0.1%
29305
 
< 0.1%
Other values (17938)20565
28.6%
ValueCountFrequency (%)
051292
71.3%
11
 
< 0.1%
115
 
< 0.1%
132
 
< 0.1%
143
 
< 0.1%
161
 
< 0.1%
175
 
< 0.1%
181
 
< 0.1%
205
 
< 0.1%
231
 
< 0.1%
ValueCountFrequency (%)
11287751
< 0.1%
11286541
< 0.1%
9635981
< 0.1%
9138051
< 0.1%
7519711
< 0.1%
7519001
< 0.1%
6886031
< 0.1%
6780191
< 0.1%
6749301
< 0.1%
6748241
< 0.1%

account_days_in_dc_12_24m
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct110
Distinct (%)0.2%
Missing8540
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean0.2271752837
Minimum0
Maximum362
Zeros63242
Zeros (%)87.9%
Negative0
Negative (%)0.0%
Memory size562.5 KiB
2021-05-19T00:29:40.794087image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum362
Range362
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.658144211
Coefficient of variation (CV)24.90651323
Kurtosis1746.878418
Mean0.2271752837
Median Absolute Deviation (MAD)0
Skewness37.44849558
Sum14412
Variance32.01459591
MonotonicityNot monotonic
2021-05-19T00:29:40.914538image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
063242
87.9%
98
 
< 0.1%
77
 
< 0.1%
427
 
< 0.1%
567
 
< 0.1%
996
 
< 0.1%
286
 
< 0.1%
435
 
< 0.1%
675
 
< 0.1%
35
 
< 0.1%
Other values (100)142
 
0.2%
(Missing)8540
 
11.9%
ValueCountFrequency (%)
063242
87.9%
11
 
< 0.1%
35
 
< 0.1%
41
 
< 0.1%
77
 
< 0.1%
98
 
< 0.1%
102
 
< 0.1%
111
 
< 0.1%
121
 
< 0.1%
133
 
< 0.1%
ValueCountFrequency (%)
3621
< 0.1%
3502
< 0.1%
3221
< 0.1%
3161
< 0.1%
2911
< 0.1%
2761
< 0.1%
2691
< 0.1%
2581
< 0.1%
2331
< 0.1%
2291
< 0.1%

account_days_in_rem_12_24m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct267
Distinct (%)0.4%
Missing8540
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean5.058638083
Minimum0
Maximum365
Zeros56481
Zeros (%)78.5%
Negative0
Negative (%)0.0%
Memory size562.5 KiB
2021-05-19T00:29:41.047418image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile31
Maximum365
Range365
Interquartile range (IQR)0

Descriptive statistics

Standard deviation22.9058832
Coefficient of variation (CV)4.528073135
Kurtosis75.8536605
Mean5.058638083
Median Absolute Deviation (MAD)0
Skewness7.503342181
Sum320920
Variance524.6794853
MonotonicityNot monotonic
2021-05-19T00:29:41.191559image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
056481
78.5%
1402
 
0.6%
2226
 
0.3%
21186
 
0.3%
15179
 
0.2%
16165
 
0.2%
3153
 
0.2%
22147
 
0.2%
14143
 
0.2%
7134
 
0.2%
Other values (257)5224
 
7.3%
(Missing)8540
 
11.9%
ValueCountFrequency (%)
056481
78.5%
1402
 
0.6%
2226
 
0.3%
3153
 
0.2%
4125
 
0.2%
565
 
0.1%
696
 
0.1%
7134
 
0.2%
8110
 
0.2%
9101
 
0.1%
ValueCountFrequency (%)
36534
< 0.1%
3621
 
< 0.1%
3581
 
< 0.1%
3561
 
< 0.1%
3542
 
< 0.1%
3511
 
< 0.1%
3461
 
< 0.1%
3411
 
< 0.1%
3371
 
< 0.1%
3292
 
< 0.1%

account_days_in_term_12_24m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct59
Distinct (%)0.1%
Missing8540
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean0.2980769231
Minimum0
Maximum97
Zeros62547
Zeros (%)86.9%
Negative0
Negative (%)0.0%
Memory size562.5 KiB
2021-05-19T00:29:41.319288image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum97
Range97
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.992377664
Coefficient of variation (CV)10.03894442
Kurtosis185.3377797
Mean0.2980769231
Median Absolute Deviation (MAD)0
Skewness12.32230452
Sum18910
Variance8.954324081
MonotonicityNot monotonic
2021-05-19T00:29:41.437959image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
062547
86.9%
34212
 
0.3%
739
 
0.1%
237
 
0.1%
132
 
< 0.1%
1131
 
< 0.1%
1530
 
< 0.1%
2128
 
< 0.1%
2327
 
< 0.1%
827
 
< 0.1%
Other values (49)430
 
0.6%
(Missing)8540
 
11.9%
ValueCountFrequency (%)
062547
86.9%
132
 
< 0.1%
237
 
0.1%
323
 
< 0.1%
418
 
< 0.1%
518
 
< 0.1%
616
 
< 0.1%
739
 
0.1%
827
 
< 0.1%
918
 
< 0.1%
ValueCountFrequency (%)
972
< 0.1%
911
 
< 0.1%
681
 
< 0.1%
671
 
< 0.1%
653
< 0.1%
641
 
< 0.1%
634
< 0.1%
601
 
< 0.1%
592
< 0.1%
544
< 0.1%

account_incoming_debt_vs_paid_0_24m
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct17632
Distinct (%)60.1%
Missing42629
Missing (%)59.2%
Infinite0
Infinite (%)0.0%
Mean1.302860834
Minimum0
Maximum3914
Zeros9460
Zeros (%)13.1%
Negative0
Negative (%)0.0%
Memory size562.5 KiB
2021-05-19T00:29:41.555250image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.1497843515
Q30.6593432561
95-th percentile2.811104884
Maximum3914
Range3914
Interquartile range (IQR)0.6593432561

Descriptive statistics

Standard deviation27.43863989
Coefficient of variation (CV)21.0602999
Kurtosis14719.3679
Mean1.302860834
Median Absolute Deviation (MAD)0.1497843515
Skewness111.6008925
Sum38240.26834
Variance752.8789588
MonotonicityNot monotonic
2021-05-19T00:29:41.674849image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09460
 
13.1%
8.0344262345
 
0.1%
2.151462995 × 10-515
 
< 0.1%
0.00430073271713
 
< 0.1%
0.000113604089712
 
< 0.1%
0.00365019011412
 
< 0.1%
0.0111248799110
 
< 0.1%
1.101746268 × 10-59
 
< 0.1%
0.035545331059
 
< 0.1%
3.168065896 × 10-59
 
< 0.1%
Other values (17622)19757
27.4%
(Missing)42629
59.2%
ValueCountFrequency (%)
09460
13.1%
3.788294926 × 10-63
 
< 0.1%
4.933934615 × 10-62
 
< 0.1%
6.034529578 × 10-61
 
< 0.1%
6.170173382 × 10-61
 
< 0.1%
6.584318786 × 10-61
 
< 0.1%
7.028048943 × 10-62
 
< 0.1%
7.389180762 × 10-61
 
< 0.1%
7.528249757 × 10-61
 
< 0.1%
7.603406326 × 10-61
 
< 0.1%
ValueCountFrequency (%)
39141
< 0.1%
1443.481
< 0.1%
1435.581
< 0.1%
1176.8888891
< 0.1%
299.73214291
< 0.1%
2941
< 0.1%
245.60869571
< 0.1%
238.57894741
< 0.1%
233.11666671
< 0.1%
201.62527721
< 0.1%

account_status
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing39100
Missing (%)54.3%
Memory size3.4 MiB
1.0
31524 
2.0
 
1352
3.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters98640
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.031524
43.8%
2.01352
 
1.9%
3.04
 
< 0.1%
(Missing)39100
54.3%

Length

2021-05-19T00:29:41.875816image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-19T00:29:41.934312image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.031524
95.9%
2.01352
 
4.1%
3.04
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
.32880
33.3%
032880
33.3%
131524
32.0%
21352
 
1.4%
34
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number65760
66.7%
Other Punctuation32880
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
032880
50.0%
131524
47.9%
21352
 
2.1%
34
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.32880
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common98640
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.32880
33.3%
032880
33.3%
131524
32.0%
21352
 
1.4%
34
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII98640
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.32880
33.3%
032880
33.3%
131524
32.0%
21352
 
1.4%
34
 
< 0.1%

account_worst_status_0_3m
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing39100
Missing (%)54.3%
Memory size3.4 MiB
1.0
27682 
2.0
4803 
3.0
 
325
4.0
 
70

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters98640
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.027682
38.5%
2.04803
 
6.7%
3.0325
 
0.5%
4.070
 
0.1%
(Missing)39100
54.3%

Length

2021-05-19T00:29:42.085288image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-19T00:29:42.147408image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.027682
84.2%
2.04803
 
14.6%
3.0325
 
1.0%
4.070
 
0.2%

Most occurring characters

ValueCountFrequency (%)
.32880
33.3%
032880
33.3%
127682
28.1%
24803
 
4.9%
3325
 
0.3%
470
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number65760
66.7%
Other Punctuation32880
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
032880
50.0%
127682
42.1%
24803
 
7.3%
3325
 
0.5%
470
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.32880
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common98640
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.32880
33.3%
032880
33.3%
127682
28.1%
24803
 
4.9%
3325
 
0.3%
470
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII98640
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.32880
33.3%
032880
33.3%
127682
28.1%
24803
 
4.9%
3325
 
0.3%
470
 
0.1%

account_worst_status_12_24m
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing48018
Missing (%)66.7%
Memory size3.2 MiB
1.0
16950 
2.0
6085 
3.0
 
728
4.0
 
199

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters71886
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.016950
 
23.5%
2.06085
 
8.5%
3.0728
 
1.0%
4.0199
 
0.3%
(Missing)48018
66.7%

Length

2021-05-19T00:29:42.319406image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-19T00:29:42.378906image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.016950
70.7%
2.06085
 
25.4%
3.0728
 
3.0%
4.0199
 
0.8%

Most occurring characters

ValueCountFrequency (%)
.23962
33.3%
023962
33.3%
116950
23.6%
26085
 
8.5%
3728
 
1.0%
4199
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number47924
66.7%
Other Punctuation23962
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
023962
50.0%
116950
35.4%
26085
 
12.7%
3728
 
1.5%
4199
 
0.4%
Other Punctuation
ValueCountFrequency (%)
.23962
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common71886
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.23962
33.3%
023962
33.3%
116950
23.6%
26085
 
8.5%
3728
 
1.0%
4199
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII71886
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.23962
33.3%
023962
33.3%
116950
23.6%
26085
 
8.5%
3728
 
1.0%
4199
 
0.3%

account_worst_status_3_6m
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing41500
Missing (%)57.7%
Memory size3.3 MiB
1.0
25444 
2.0
4555 
3.0
 
376
4.0
 
105

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters91440
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.025444
35.3%
2.04555
 
6.3%
3.0376
 
0.5%
4.0105
 
0.1%
(Missing)41500
57.7%

Length

2021-05-19T00:29:42.533156image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-19T00:29:42.592035image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.025444
83.5%
2.04555
 
14.9%
3.0376
 
1.2%
4.0105
 
0.3%

Most occurring characters

ValueCountFrequency (%)
.30480
33.3%
030480
33.3%
125444
27.8%
24555
 
5.0%
3376
 
0.4%
4105
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number60960
66.7%
Other Punctuation30480
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
030480
50.0%
125444
41.7%
24555
 
7.5%
3376
 
0.6%
4105
 
0.2%
Other Punctuation
ValueCountFrequency (%)
.30480
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common91440
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.30480
33.3%
030480
33.3%
125444
27.8%
24555
 
5.0%
3376
 
0.4%
4105
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII91440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.30480
33.3%
030480
33.3%
125444
27.8%
24555
 
5.0%
3376
 
0.4%
4105
 
0.1%

account_worst_status_6_12m
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing43415
Missing (%)60.3%
Memory size3.3 MiB
1.0
22129 
2.0
5770 
3.0
 
508
4.0
 
158

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters85695
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.022129
30.7%
2.05770
 
8.0%
3.0508
 
0.7%
4.0158
 
0.2%
(Missing)43415
60.3%

Length

2021-05-19T00:29:42.749434image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-19T00:29:42.806620image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.022129
77.5%
2.05770
 
20.2%
3.0508
 
1.8%
4.0158
 
0.6%

Most occurring characters

ValueCountFrequency (%)
.28565
33.3%
028565
33.3%
122129
25.8%
25770
 
6.7%
3508
 
0.6%
4158
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number57130
66.7%
Other Punctuation28565
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
028565
50.0%
122129
38.7%
25770
 
10.1%
3508
 
0.9%
4158
 
0.3%
Other Punctuation
ValueCountFrequency (%)
.28565
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common85695
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.28565
33.3%
028565
33.3%
122129
25.8%
25770
 
6.7%
3508
 
0.6%
4158
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII85695
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.28565
33.3%
028565
33.3%
122129
25.8%
25770
 
6.7%
3508
 
0.6%
4158
 
0.2%

age
Real number (ℝ≥0)

Distinct76
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.99709642
Minimum18
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size562.5 KiB
2021-05-19T00:29:42.895631image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile19
Q125
median34
Q345
95-th percentile60
Maximum100
Range82
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.97373151
Coefficient of variation (CV)0.3604104997
Kurtosis-0.04685975442
Mean35.99709642
Median Absolute Deviation (MAD)10
Skewness0.689950009
Sum2591071
Variance168.3177092
MonotonicityNot monotonic
2021-05-19T00:29:43.028169image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
182629
 
3.7%
222502
 
3.5%
212489
 
3.5%
232356
 
3.3%
202328
 
3.2%
242190
 
3.0%
252181
 
3.0%
282174
 
3.0%
302147
 
3.0%
292140
 
3.0%
Other values (66)48844
67.9%
ValueCountFrequency (%)
182629
3.7%
191909
2.7%
202328
3.2%
212489
3.5%
222502
3.5%
232356
3.3%
242190
3.0%
252181
3.0%
262104
2.9%
272125
3.0%
ValueCountFrequency (%)
1001
 
< 0.1%
951
 
< 0.1%
931
 
< 0.1%
911
 
< 0.1%
892
 
< 0.1%
884
 
< 0.1%
874
 
< 0.1%
8612
< 0.1%
859
< 0.1%
848
< 0.1%

avg_payment_span_0_12m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6823
Distinct (%)12.4%
Missing17086
Missing (%)23.7%
Infinite0
Infinite (%)0.0%
Mean17.95736194
Minimum0
Maximum260
Zeros364
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size562.5 KiB
2021-05-19T00:29:43.151274image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.074306122
Q110.8
median14.875
Q321
95-th percentile41
Maximum260
Range260
Interquartile range (IQR)10.2

Descriptive statistics

Standard deviation12.78122679
Coefficient of variation (CV)0.7117541444
Kurtosis21.70483658
Mean17.95736194
Median Absolute Deviation (MAD)4.875
Skewness3.270446315
Sum985751.4265
Variance163.3597581
MonotonicityNot monotonic
2021-05-19T00:29:43.284641image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
141521
 
2.1%
131328
 
1.8%
12892
 
1.2%
15887
 
1.2%
16842
 
1.2%
11777
 
1.1%
10759
 
1.1%
9692
 
1.0%
17682
 
0.9%
7633
 
0.9%
Other values (6813)45881
63.7%
(Missing)17086
 
23.7%
ValueCountFrequency (%)
0364
0.5%
0.16666666672
 
< 0.1%
0.21
 
< 0.1%
0.22222222221
 
< 0.1%
0.253
 
< 0.1%
0.33333333339
 
< 0.1%
0.3751
 
< 0.1%
0.531
 
< 0.1%
0.51612903232
 
< 0.1%
0.55172413792
 
< 0.1%
ValueCountFrequency (%)
2601
< 0.1%
2241
< 0.1%
2041
< 0.1%
1871
< 0.1%
1841
< 0.1%
1821
< 0.1%
1741
< 0.1%
1691
< 0.1%
1671
< 0.1%
1661
< 0.1%

avg_payment_span_0_3m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1981
Distinct (%)5.4%
Missing35356
Missing (%)49.1%
Infinite0
Infinite (%)0.0%
Mean14.96032523
Minimum0
Maximum84
Zeros584
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size562.5 KiB
2021-05-19T00:29:43.405381image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q18.469607843
median13
Q318.2
95-th percentile36
Maximum84
Range84
Interquartile range (IQR)9.730392157

Descriptive statistics

Standard deviation10.21566225
Coefficient of variation (CV)0.6828502786
Kurtosis4.692599538
Mean14.96032523
Median Absolute Deviation (MAD)5
Skewness1.762772749
Sum547906.9513
Variance104.3597552
MonotonicityNot monotonic
2021-05-19T00:29:43.515079image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
142013
 
2.8%
131666
 
2.3%
12954
 
1.3%
16954
 
1.3%
7949
 
1.3%
6945
 
1.3%
15911
 
1.3%
10892
 
1.2%
11888
 
1.2%
9823
 
1.1%
Other values (1971)25629
35.6%
(Missing)35356
49.1%
ValueCountFrequency (%)
0584
0.8%
0.16666666672
 
< 0.1%
0.21
 
< 0.1%
0.252
 
< 0.1%
0.28571428571
 
< 0.1%
0.33333333338
 
< 0.1%
0.36363636361
 
< 0.1%
0.38888888891
 
< 0.1%
0.41
 
< 0.1%
0.41176470591
 
< 0.1%
ValueCountFrequency (%)
844
< 0.1%
834
< 0.1%
811
 
< 0.1%
802
 
< 0.1%
782
 
< 0.1%
77.51
 
< 0.1%
773
< 0.1%
765
< 0.1%
755
< 0.1%
743
< 0.1%

default
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
0.0
70950 
1.0
 
1030

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters215940
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.070950
98.6%
1.01030
 
1.4%

Length

2021-05-19T00:29:43.704222image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-19T00:29:43.761485image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.070950
98.6%
1.01030
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0142930
66.2%
.71980
33.3%
11030
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number143960
66.7%
Other Punctuation71980
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0142930
99.3%
11030
 
0.7%
Other Punctuation
ValueCountFrequency (%)
.71980
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common215940
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0142930
66.2%
.71980
33.3%
11030
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII215940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0142930
66.2%
.71980
33.3%
11030
 
0.5%

has_paid
Boolean

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size70.4 KiB
True
61327 
False
10653 
ValueCountFrequency (%)
True61327
85.2%
False10653
 
14.8%
2021-05-19T00:29:43.794109image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

max_paid_inv_0_12m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct10969
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9253.319533
Minimum0
Maximum279000
Zeros15535
Zeros (%)21.6%
Negative0
Negative (%)0.0%
Memory size562.5 KiB
2021-05-19T00:29:43.867546image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12075
median6070
Q311390
95-th percentile29500
Maximum279000
Range279000
Interquartile range (IQR)9315

Descriptive statistics

Standard deviation13607.10088
Coefficient of variation (CV)1.470510214
Kurtosis55.60228917
Mean9253.319533
Median Absolute Deviation (MAD)4780
Skewness5.638095311
Sum666053940
Variance185153194.4
MonotonicityNot monotonic
2021-05-19T00:29:43.990249image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
015535
 
21.6%
5290306
 
0.4%
895289
 
0.4%
4290264
 
0.4%
5000202
 
0.3%
6790202
 
0.3%
4790197
 
0.3%
3290197
 
0.3%
2290186
 
0.3%
6290174
 
0.2%
Other values (10959)54428
75.6%
ValueCountFrequency (%)
015535
21.6%
901
 
< 0.1%
1751
 
< 0.1%
2101
 
< 0.1%
2702
 
< 0.1%
2901
 
< 0.1%
2952
 
< 0.1%
3003
 
< 0.1%
3201
 
< 0.1%
3602
 
< 0.1%
ValueCountFrequency (%)
2790001
 
< 0.1%
2702951
 
< 0.1%
2643002
 
< 0.1%
2603951
 
< 0.1%
2451102
 
< 0.1%
2357902
 
< 0.1%
2338905
< 0.1%
2305451
 
< 0.1%
2298501
 
< 0.1%
2196002
 
< 0.1%

max_paid_inv_0_24m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct11389
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11276.92827
Minimum0
Maximum279000
Zeros12626
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size562.5 KiB
2021-05-19T00:29:44.118006image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13374.5
median7590
Q313865
95-th percentile34500
Maximum279000
Range279000
Interquartile range (IQR)10490.5

Descriptive statistics

Standard deviation15312.45653
Coefficient of variation (CV)1.357857048
Kurtosis43.57179086
Mean11276.92827
Median Absolute Deviation (MAD)5020
Skewness5.057089714
Sum811713297
Variance234471325
MonotonicityNot monotonic
2021-05-19T00:29:44.230150image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
012626
 
17.5%
5290305
 
0.4%
4290201
 
0.3%
3290183
 
0.3%
6290180
 
0.3%
5000178
 
0.2%
9290176
 
0.2%
6790173
 
0.2%
895167
 
0.2%
4790158
 
0.2%
Other values (11379)57633
80.1%
ValueCountFrequency (%)
012626
17.5%
901
 
< 0.1%
2101
 
< 0.1%
2701
 
< 0.1%
2952
 
< 0.1%
3003
 
< 0.1%
3201
 
< 0.1%
3701
 
< 0.1%
3801
 
< 0.1%
3905
 
< 0.1%
ValueCountFrequency (%)
2790001
 
< 0.1%
2702951
 
< 0.1%
2643002
 
< 0.1%
2603951
 
< 0.1%
2451102
 
< 0.1%
2357902
 
< 0.1%
2349951
 
< 0.1%
2338905
< 0.1%
2305455
< 0.1%
2298501
 
< 0.1%

merchant_category
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
Diversified entertainment
27852 
Youthful Shoes & Clothing
8335 
Books & Magazines
6733 
General Shoes & Clothing
3359 
Concept stores & Miscellaneous
3135 
Other values (51)
22566 

Length

Max length55
Median length25
Mean length23.57557655
Min length3

Characters and Unicode

Total characters1696970
Distinct characters55
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDiversified entertainment
2nd rowYouthful Shoes & Clothing
3rd rowDiversified entertainment
4th rowDiversified entertainment
5th rowSports gear & Outdoor

Common Values

ValueCountFrequency (%)
Diversified entertainment27852
38.7%
Youthful Shoes & Clothing8335
 
11.6%
Books & Magazines6733
 
9.4%
General Shoes & Clothing3359
 
4.7%
Concept stores & Miscellaneous3135
 
4.4%
Sports gear & Outdoor2666
 
3.7%
Dietary supplements2219
 
3.1%
Diversified children products2171
 
3.0%
Diversified electronics1325
 
1.8%
Prints & Photos1200
 
1.7%
Other values (46)12985
18.0%

Length

2021-05-19T00:29:44.490802image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
diversified32936
16.1%
32709
16.0%
entertainment27852
13.6%
clothing11976
 
5.8%
shoes11918
 
5.8%
youthful8335
 
4.1%
books6733
 
3.3%
magazines6733
 
3.3%
products4666
 
2.3%
children3713
 
1.8%
Other values (89)57403
28.0%

Most occurring characters

ValueCountFrequency (%)
e230995
13.6%
i172360
 
10.2%
t145418
 
8.6%
132994
 
7.8%
n126983
 
7.5%
r104344
 
6.1%
s104043
 
6.1%
o85781
 
5.1%
a65323
 
3.8%
d47736
 
2.8%
Other values (45)480993
28.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1410397
83.1%
Space Separator132994
 
7.8%
Uppercase Letter120644
 
7.1%
Other Punctuation32789
 
1.9%
Open Punctuation73
 
< 0.1%
Close Punctuation73
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e230995
16.4%
i172360
12.2%
t145418
10.3%
n126983
9.0%
r104344
 
7.4%
s104043
 
7.4%
o85781
 
6.1%
a65323
 
4.6%
d47736
 
3.4%
l45826
 
3.2%
Other values (15)281588
20.0%
Uppercase Letter
ValueCountFrequency (%)
D36311
30.1%
C19056
15.8%
S14665
12.2%
M10094
 
8.4%
B8442
 
7.0%
Y8335
 
6.9%
P5245
 
4.3%
G4358
 
3.6%
H2775
 
2.3%
O2675
 
2.2%
Other values (13)8688
 
7.2%
Other Punctuation
ValueCountFrequency (%)
&32709
99.8%
/64
 
0.2%
.9
 
< 0.1%
,7
 
< 0.1%
Space Separator
ValueCountFrequency (%)
132994
100.0%
Open Punctuation
ValueCountFrequency (%)
(73
100.0%
Close Punctuation
ValueCountFrequency (%)
)73
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1531041
90.2%
Common165929
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e230995
15.1%
i172360
11.3%
t145418
 
9.5%
n126983
 
8.3%
r104344
 
6.8%
s104043
 
6.8%
o85781
 
5.6%
a65323
 
4.3%
d47736
 
3.1%
l45826
 
3.0%
Other values (38)402232
26.3%
Common
ValueCountFrequency (%)
132994
80.2%
&32709
 
19.7%
(73
 
< 0.1%
)73
 
< 0.1%
/64
 
< 0.1%
.9
 
< 0.1%
,7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1696970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e230995
13.6%
i172360
 
10.2%
t145418
 
8.6%
132994
 
7.8%
n126983
 
7.5%
r104344
 
6.1%
s104043
 
6.1%
o85781
 
5.1%
a65323
 
3.8%
d47736
 
2.8%
Other values (45)480993
28.3%

merchant_group
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
Entertainment
35151 
Clothing & Shoes
11979 
Leisure, Sport & Hobby
7895 
Health & Beauty
5302 
Children Products
3713 
Other values (7)
7940 

Length

Max length22
Median length13
Mean length15.01772715
Min length11

Characters and Unicode

Total characters1080976
Distinct characters36
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntertainment
2nd rowClothing & Shoes
3rd rowEntertainment
4th rowEntertainment
5th rowLeisure, Sport & Hobby

Common Values

ValueCountFrequency (%)
Entertainment35151
48.8%
Clothing & Shoes11979
 
16.6%
Leisure, Sport & Hobby7895
 
11.0%
Health & Beauty5302
 
7.4%
Children Products3713
 
5.2%
Home & Garden2701
 
3.8%
Electronics2182
 
3.0%
Intangible products813
 
1.1%
Jewelry & Accessories757
 
1.1%
Automotive Products676
 
0.9%
Other values (2)811
 
1.1%

Length

2021-05-19T00:29:44.710907image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
entertainment35151
24.5%
28905
20.2%
clothing11979
 
8.4%
shoes11979
 
8.4%
sport7895
 
5.5%
leisure7895
 
5.5%
hobby7895
 
5.5%
beauty5302
 
3.7%
health5302
 
3.7%
products5202
 
3.6%
Other values (12)15922
11.1%

Most occurring characters

ValueCountFrequency (%)
t146560
13.6%
n127654
11.8%
e125842
11.6%
71447
 
6.6%
r67604
 
6.3%
i64246
 
5.9%
o53024
 
4.9%
a50620
 
4.7%
m38528
 
3.6%
E37873
 
3.5%
Other values (26)297578
27.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter859020
79.5%
Uppercase Letter113709
 
10.5%
Space Separator71447
 
6.6%
Other Punctuation36800
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t146560
17.1%
n127654
14.9%
e125842
14.6%
r67604
7.9%
i64246
7.5%
o53024
 
6.2%
a50620
 
5.9%
m38528
 
4.5%
h32973
 
3.8%
s30069
 
3.5%
Other values (10)121900
14.2%
Uppercase Letter
ValueCountFrequency (%)
E37873
33.3%
S19874
17.5%
H15898
14.0%
C15692
13.8%
L7895
 
6.9%
B5573
 
4.9%
P4389
 
3.9%
G2701
 
2.4%
A1433
 
1.3%
I813
 
0.7%
Other values (3)1568
 
1.4%
Other Punctuation
ValueCountFrequency (%)
&28905
78.5%
,7895
 
21.5%
Space Separator
ValueCountFrequency (%)
71447
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin972729
90.0%
Common108247
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t146560
15.1%
n127654
13.1%
e125842
12.9%
r67604
 
6.9%
i64246
 
6.6%
o53024
 
5.5%
a50620
 
5.2%
m38528
 
4.0%
E37873
 
3.9%
h32973
 
3.4%
Other values (23)227805
23.4%
Common
ValueCountFrequency (%)
71447
66.0%
&28905
26.7%
,7895
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1080976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t146560
13.6%
n127654
11.8%
e125842
11.6%
71447
 
6.6%
r67604
 
6.3%
i64246
 
5.9%
o53024
 
4.9%
a50620
 
4.7%
m38528
 
3.6%
E37873
 
3.5%
Other values (26)297578
27.5%

name_in_email
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
F+L
29092 
no_match
12149 
L1+F
11553 
F
7012 
Nick
5968 
Other values (3)
6206 

Length

Max length8
Median length3
Mean length3.940733537
Min length1

Characters and Unicode

Total characters283654
Distinct characters18
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowL1+F
2nd rowF1+L
3rd rowno_match
4th rowF+L
5th rowno_match

Common Values

ValueCountFrequency (%)
F+L29092
40.4%
no_match12149
16.9%
L1+F11553
 
16.1%
F7012
 
9.7%
Nick5968
 
8.3%
F1+L5255
 
7.3%
L934
 
1.3%
Initials17
 
< 0.1%

Length

2021-05-19T00:29:44.902626image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-19T00:29:44.974640image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
f+l29092
40.4%
no_match12149
16.9%
l1+f11553
 
16.1%
f7012
 
9.7%
nick5968
 
8.3%
f1+l5255
 
7.3%
l934
 
1.3%
initials17
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
F52912
18.7%
L46834
16.5%
+45900
16.2%
c18117
 
6.4%
116808
 
5.9%
n12166
 
4.3%
a12166
 
4.3%
t12166
 
4.3%
o12149
 
4.3%
_12149
 
4.3%
Other values (8)42287
14.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter105731
37.3%
Lowercase Letter103066
36.3%
Math Symbol45900
16.2%
Decimal Number16808
 
5.9%
Connector Punctuation12149
 
4.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c18117
17.6%
n12166
11.8%
a12166
11.8%
t12166
11.8%
o12149
11.8%
m12149
11.8%
h12149
11.8%
i6002
 
5.8%
k5968
 
5.8%
l17
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
F52912
50.0%
L46834
44.3%
N5968
 
5.6%
I17
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
116808
100.0%
Math Symbol
ValueCountFrequency (%)
+45900
100.0%
Connector Punctuation
ValueCountFrequency (%)
_12149
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin208797
73.6%
Common74857
 
26.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
F52912
25.3%
L46834
22.4%
c18117
 
8.7%
n12166
 
5.8%
a12166
 
5.8%
t12166
 
5.8%
o12149
 
5.8%
m12149
 
5.8%
h12149
 
5.8%
i6002
 
2.9%
Other values (5)11987
 
5.7%
Common
ValueCountFrequency (%)
+45900
61.3%
116808
 
22.5%
_12149
 
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII283654
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F52912
18.7%
L46834
16.5%
+45900
16.2%
c18117
 
6.4%
116808
 
5.9%
n12166
 
4.3%
a12166
 
4.3%
t12166
 
4.3%
o12149
 
4.3%
_12149
 
4.3%
Other values (8)42287
14.9%

num_active_div_by_paid_inv_0_12m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct757
Distinct (%)1.4%
Missing16438
Missing (%)22.8%
Infinite0
Infinite (%)0.0%
Mean0.1145715226
Minimum0
Maximum9
Zeros35071
Zeros (%)48.7%
Negative0
Negative (%)0.0%
Memory size562.5 KiB
2021-05-19T00:29:45.077193image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.1
95-th percentile0.5
Maximum9
Range9
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.2937883913
Coefficient of variation (CV)2.564235726
Kurtosis89.17913151
Mean0.1145715226
Median Absolute Deviation (MAD)0
Skewness6.552729123
Sum6363.531506
Variance0.08631161886
MonotonicityNot monotonic
2021-05-19T00:29:45.188612image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
035071
48.7%
11746
 
2.4%
0.51605
 
2.2%
0.33333333331405
 
2.0%
0.251188
 
1.7%
0.21084
 
1.5%
0.1666666667925
 
1.3%
0.1428571429790
 
1.1%
0.125655
 
0.9%
0.1111111111607
 
0.8%
Other values (747)10466
 
14.5%
(Missing)16438
22.8%
ValueCountFrequency (%)
035071
48.7%
0.0066666666671
 
< 0.1%
0.0071428571431
 
< 0.1%
0.0072992700732
 
< 0.1%
0.0075187969921
 
< 0.1%
0.0079365079374
 
< 0.1%
0.0084745762711
 
< 0.1%
0.0085470085471
 
< 0.1%
0.0091743119273
 
< 0.1%
0.0092592592593
 
< 0.1%
ValueCountFrequency (%)
92
 
< 0.1%
81
 
< 0.1%
71
 
< 0.1%
63
 
< 0.1%
58
 
< 0.1%
4.51
 
< 0.1%
49
 
< 0.1%
3.52
 
< 0.1%
353
0.1%
2.56
 
< 0.1%

num_active_inv
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct36
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5955959989
Minimum0
Maximum38
Zeros49970
Zeros (%)69.4%
Negative0
Negative (%)0.0%
Memory size562.5 KiB
2021-05-19T00:29:45.293357image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum38
Range38
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.510762935
Coefficient of variation (CV)2.536556554
Kurtosis101.1680825
Mean0.5955959989
Median Absolute Deviation (MAD)0
Skewness7.632149593
Sum42871
Variance2.282404645
MonotonicityNot monotonic
2021-05-19T00:29:45.413317image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
049970
69.4%
113351
 
18.5%
24566
 
6.3%
31840
 
2.6%
4864
 
1.2%
5462
 
0.6%
6274
 
0.4%
7155
 
0.2%
8115
 
0.2%
980
 
0.1%
Other values (26)303
 
0.4%
ValueCountFrequency (%)
049970
69.4%
113351
 
18.5%
24566
 
6.3%
31840
 
2.6%
4864
 
1.2%
5462
 
0.6%
6274
 
0.4%
7155
 
0.2%
8115
 
0.2%
980
 
0.1%
ValueCountFrequency (%)
381
 
< 0.1%
372
 
< 0.1%
351
 
< 0.1%
333
 
< 0.1%
311
 
< 0.1%
301
 
< 0.1%
292
 
< 0.1%
284
< 0.1%
275
< 0.1%
269
< 0.1%

num_arch_dc_0_12m
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0614059461
Minimum0
Maximum16
Zeros68934
Zeros (%)95.8%
Negative0
Negative (%)0.0%
Memory size562.5 KiB
2021-05-19T00:29:45.519150image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum16
Range16
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3744909662
Coefficient of variation (CV)6.098610803
Kurtosis261.3837717
Mean0.0614059461
Median Absolute Deviation (MAD)0
Skewness12.32458835
Sum4420
Variance0.1402434838
MonotonicityNot monotonic
2021-05-19T00:29:45.617155image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
068934
95.8%
12292
 
3.2%
2483
 
0.7%
3141
 
0.2%
453
 
0.1%
628
 
< 0.1%
522
 
< 0.1%
711
 
< 0.1%
104
 
< 0.1%
134
 
< 0.1%
Other values (4)8
 
< 0.1%
ValueCountFrequency (%)
068934
95.8%
12292
 
3.2%
2483
 
0.7%
3141
 
0.2%
453
 
0.1%
522
 
< 0.1%
628
 
< 0.1%
711
 
< 0.1%
83
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
161
 
< 0.1%
134
 
< 0.1%
112
 
< 0.1%
104
 
< 0.1%
92
 
< 0.1%
83
 
< 0.1%
711
 
< 0.1%
628
< 0.1%
522
< 0.1%
453
0.1%

num_arch_dc_12_24m
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05990552931
Minimum0
Maximum13
Zeros69104
Zeros (%)96.0%
Negative0
Negative (%)0.0%
Memory size562.5 KiB
2021-05-19T00:29:45.710040image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3714570587
Coefficient of variation (CV)6.200714074
Kurtosis193.7285193
Mean0.05990552931
Median Absolute Deviation (MAD)0
Skewness11.15982659
Sum4312
Variance0.1379803465
MonotonicityNot monotonic
2021-05-19T00:29:45.804240image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
069104
96.0%
12092
 
2.9%
2488
 
0.7%
3141
 
0.2%
475
 
0.1%
535
 
< 0.1%
716
 
< 0.1%
613
 
< 0.1%
106
 
< 0.1%
84
 
< 0.1%
Other values (3)6
 
< 0.1%
ValueCountFrequency (%)
069104
96.0%
12092
 
2.9%
2488
 
0.7%
3141
 
0.2%
475
 
0.1%
535
 
< 0.1%
613
 
< 0.1%
716
 
< 0.1%
84
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
131
 
< 0.1%
113
 
< 0.1%
106
 
< 0.1%
92
 
< 0.1%
84
 
< 0.1%
716
 
< 0.1%
613
 
< 0.1%
535
 
< 0.1%
475
0.1%
3141
0.2%

num_arch_ok_0_12m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct193
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.306335093
Minimum0
Maximum248
Zeros19662
Zeros (%)27.3%
Negative0
Negative (%)0.0%
Memory size562.5 KiB
2021-05-19T00:29:45.911174image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q37
95-th percentile31
Maximum248
Range248
Interquartile range (IQR)7

Descriptive statistics

Standard deviation15.93694826
Coefficient of variation (CV)2.181250662
Kurtosis43.83347953
Mean7.306335093
Median Absolute Deviation (MAD)2
Skewness5.596989712
Sum525910
Variance253.9863198
MonotonicityNot monotonic
2021-05-19T00:29:46.037640image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019662
27.3%
110048
14.0%
27139
 
9.9%
35340
 
7.4%
44168
 
5.8%
53408
 
4.7%
62783
 
3.9%
72225
 
3.1%
81813
 
2.5%
91587
 
2.2%
Other values (183)13807
19.2%
ValueCountFrequency (%)
019662
27.3%
110048
14.0%
27139
 
9.9%
35340
 
7.4%
44168
 
5.8%
53408
 
4.7%
62783
 
3.9%
72225
 
3.1%
81813
 
2.5%
91587
 
2.2%
ValueCountFrequency (%)
2481
 
< 0.1%
2471
 
< 0.1%
2311
 
< 0.1%
2254
< 0.1%
2241
 
< 0.1%
2234
< 0.1%
2224
< 0.1%
2212
< 0.1%
2204
< 0.1%
2193
< 0.1%

num_arch_ok_12_24m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct194
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.373978883
Minimum0
Maximum313
Zeros27188
Zeros (%)37.8%
Negative0
Negative (%)0.0%
Memory size562.5 KiB
2021-05-19T00:29:46.159789image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile28
Maximum313
Range313
Interquartile range (IQR)6

Descriptive statistics

Standard deviation15.26527626
Coefficient of variation (CV)2.394936748
Kurtosis81.48912307
Mean6.373978883
Median Absolute Deviation (MAD)2
Skewness7.073678481
Sum458799
Variance233.0286592
MonotonicityNot monotonic
2021-05-19T00:29:46.281233image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
027188
37.8%
18005
 
11.1%
25911
 
8.2%
34563
 
6.3%
43510
 
4.9%
52963
 
4.1%
62449
 
3.4%
71986
 
2.8%
81703
 
2.4%
91379
 
1.9%
Other values (184)12323
17.1%
ValueCountFrequency (%)
027188
37.8%
18005
 
11.1%
25911
 
8.2%
34563
 
6.3%
43510
 
4.9%
52963
 
4.1%
62449
 
3.4%
71986
 
2.8%
81703
 
2.4%
91379
 
1.9%
ValueCountFrequency (%)
3131
 
< 0.1%
3041
 
< 0.1%
3021
 
< 0.1%
3011
 
< 0.1%
2934
< 0.1%
2922
< 0.1%
2901
 
< 0.1%
2881
 
< 0.1%
2781
 
< 0.1%
2772
< 0.1%

num_arch_rem_0_12m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4661433732
Minimum0
Maximum42
Zeros55234
Zeros (%)76.7%
Negative0
Negative (%)0.0%
Memory size562.5 KiB
2021-05-19T00:29:46.396011image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum42
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.337337993
Coefficient of variation (CV)2.868941339
Kurtosis133.1969629
Mean0.4661433732
Median Absolute Deviation (MAD)0
Skewness8.33316867
Sum33553
Variance1.788472907
MonotonicityNot monotonic
2021-05-19T00:29:46.497745image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
055234
76.7%
19698
 
13.5%
23484
 
4.8%
31623
 
2.3%
4820
 
1.1%
5409
 
0.6%
6219
 
0.3%
7157
 
0.2%
884
 
0.1%
960
 
0.1%
Other values (21)192
 
0.3%
ValueCountFrequency (%)
055234
76.7%
19698
 
13.5%
23484
 
4.8%
31623
 
2.3%
4820
 
1.1%
5409
 
0.6%
6219
 
0.3%
7157
 
0.2%
884
 
0.1%
960
 
0.1%
ValueCountFrequency (%)
423
 
< 0.1%
391
 
< 0.1%
296
< 0.1%
272
 
< 0.1%
264
 
< 0.1%
252
 
< 0.1%
2412
< 0.1%
235
< 0.1%
221
 
< 0.1%
217
< 0.1%

num_arch_written_off_0_12m
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing12957
Missing (%)18.0%
Memory size3.9 MiB
0.0
59018 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters177069
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.059018
82.0%
1.05
 
< 0.1%
(Missing)12957
 
18.0%

Length

2021-05-19T00:29:46.695201image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-19T00:29:46.753955image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.059018
> 99.9%
1.05
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0118041
66.7%
.59023
33.3%
15
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number118046
66.7%
Other Punctuation59023
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0118041
> 99.9%
15
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.59023
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common177069
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0118041
66.7%
.59023
33.3%
15
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII177069
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0118041
66.7%
.59023
33.3%
15
 
< 0.1%

num_arch_written_off_12_24m
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing12957
Missing (%)18.0%
Memory size3.9 MiB
0.0
59015 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters177069
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.059015
82.0%
1.08
 
< 0.1%
(Missing)12957
 
18.0%

Length

2021-05-19T00:29:46.908475image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-19T00:29:46.966675image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.059015
> 99.9%
1.08
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0118038
66.7%
.59023
33.3%
18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number118046
66.7%
Other Punctuation59023
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0118038
> 99.9%
18
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.59023
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common177069
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0118038
66.7%
.59023
33.3%
18
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII177069
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0118038
66.7%
.59023
33.3%
18
 
< 0.1%

num_unpaid_bills
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct125
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.129035843
Minimum0
Maximum182
Zeros37379
Zeros (%)51.9%
Negative0
Negative (%)0.0%
Memory size562.5 KiB
2021-05-19T00:29:47.043368image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile10
Maximum182
Range182
Interquartile range (IQR)2

Descriptive statistics

Standard deviation6.182115901
Coefficient of variation (CV)2.903716215
Kurtosis164.6684265
Mean2.129035843
Median Absolute Deviation (MAD)0
Skewness10.08719976
Sum153248
Variance38.21855701
MonotonicityNot monotonic
2021-05-19T00:29:47.962029image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
037379
51.9%
113936
 
19.4%
26667
 
9.3%
33628
 
5.0%
42178
 
3.0%
51471
 
2.0%
61075
 
1.5%
7838
 
1.2%
8637
 
0.9%
9556
 
0.8%
Other values (115)3615
 
5.0%
ValueCountFrequency (%)
037379
51.9%
113936
 
19.4%
26667
 
9.3%
33628
 
5.0%
42178
 
3.0%
51471
 
2.0%
61075
 
1.5%
7838
 
1.2%
8637
 
0.9%
9556
 
0.8%
ValueCountFrequency (%)
1821
< 0.1%
1601
< 0.1%
1591
< 0.1%
1581
< 0.1%
1531
< 0.1%
1521
< 0.1%
1491
< 0.1%
1471
< 0.1%
1461
< 0.1%
1451
< 0.1%

recovery_debt
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct87
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.592095026
Minimum0
Maximum11190
Zeros71821
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size562.5 KiB
2021-05-19T00:29:48.086658image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum11190
Range11190
Interquartile range (IQR)0

Descriptive statistics

Standard deviation106.7059935
Coefficient of variation (CV)29.70578249
Kurtosis3564.299923
Mean3.592095026
Median Absolute Deviation (MAD)0
Skewness51.15070937
Sum258559
Variance11386.16906
MonotonicityNot monotonic
2021-05-19T00:29:48.206711image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
071821
99.8%
50030
 
< 0.1%
100018
 
< 0.1%
15006
 
< 0.1%
21904
 
< 0.1%
6013
 
< 0.1%
25803
 
< 0.1%
19393
 
< 0.1%
10183
 
< 0.1%
26122
 
< 0.1%
Other values (77)87
 
0.1%
ValueCountFrequency (%)
071821
99.8%
471
 
< 0.1%
901
 
< 0.1%
991
 
< 0.1%
3481
 
< 0.1%
50030
 
< 0.1%
5301
 
< 0.1%
5312
 
< 0.1%
5631
 
< 0.1%
5731
 
< 0.1%
ValueCountFrequency (%)
111901
< 0.1%
79101
< 0.1%
72002
< 0.1%
66301
< 0.1%
62851
< 0.1%
60651
< 0.1%
55901
< 0.1%
49001
< 0.1%
39851
< 0.1%
38951
< 0.1%

row_id
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct71980
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45056.1569
Minimum0
Maximum89975
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size562.5 KiB
2021-05-19T00:29:48.330315image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4538.9
Q122623.5
median45056.5
Q367485.75
95-th percentile85479.05
Maximum89975
Range89975
Interquartile range (IQR)44862.25

Descriptive statistics

Standard deviation25935.85702
Coefficient of variation (CV)0.5756340266
Kurtosis-1.196899398
Mean45056.1569
Median Absolute Deviation (MAD)22432
Skewness-0.003382878361
Sum3243142174
Variance672668679.4
MonotonicityNot monotonic
2021-05-19T00:29:48.445734image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
33711
 
< 0.1%
504821
 
< 0.1%
566251
 
< 0.1%
545761
 
< 0.1%
95181
 
< 0.1%
156611
 
< 0.1%
136121
 
< 0.1%
13221
 
< 0.1%
586781
 
< 0.1%
Other values (71970)71970
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
31
< 0.1%
41
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
899751
< 0.1%
899741
< 0.1%
899721
< 0.1%
899691
< 0.1%
899681
< 0.1%
899671
< 0.1%
899651
< 0.1%
899641
< 0.1%
899631
< 0.1%
899621
< 0.1%

status_2nd_last_archived_0_24m
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
1
45280 
0
20052 
2
5585 
3
 
1062
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters71980
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
145280
62.9%
020052
27.9%
25585
 
7.8%
31062
 
1.5%
51
 
< 0.1%

Length

2021-05-19T00:29:48.635974image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-19T00:29:48.698466image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
145280
62.9%
020052
27.9%
25585
 
7.8%
31062
 
1.5%
51
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
145280
62.9%
020052
27.9%
25585
 
7.8%
31062
 
1.5%
51
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number71980
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
145280
62.9%
020052
27.9%
25585
 
7.8%
31062
 
1.5%
51
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common71980
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
145280
62.9%
020052
27.9%
25585
 
7.8%
31062
 
1.5%
51
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII71980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
145280
62.9%
020052
27.9%
25585
 
7.8%
31062
 
1.5%
51
 
< 0.1%

status_3rd_last_archived_0_24m
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
1
41183 
0
24953 
2
4927 
3
 
914
5
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters71980
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
141183
57.2%
024953
34.7%
24927
 
6.8%
3914
 
1.3%
53
 
< 0.1%

Length

2021-05-19T00:29:48.853293image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-19T00:29:48.918165image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
141183
57.2%
024953
34.7%
24927
 
6.8%
3914
 
1.3%
53
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
141183
57.2%
024953
34.7%
24927
 
6.8%
3914
 
1.3%
53
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number71980
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
141183
57.2%
024953
34.7%
24927
 
6.8%
3914
 
1.3%
53
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common71980
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
141183
57.2%
024953
34.7%
24927
 
6.8%
3914
 
1.3%
53
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII71980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
141183
57.2%
024953
34.7%
24927
 
6.8%
3914
 
1.3%
53
 
< 0.1%

status_last_archived_0_24m
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
1
51806 
0
13430 
2
5617 
3
 
1127

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters71980
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
151806
72.0%
013430
 
18.7%
25617
 
7.8%
31127
 
1.6%

Length

2021-05-19T00:29:49.113617image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-19T00:29:49.180773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
151806
72.0%
013430
 
18.7%
25617
 
7.8%
31127
 
1.6%

Most occurring characters

ValueCountFrequency (%)
151806
72.0%
013430
 
18.7%
25617
 
7.8%
31127
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number71980
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
151806
72.0%
013430
 
18.7%
25617
 
7.8%
31127
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common71980
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
151806
72.0%
013430
 
18.7%
25617
 
7.8%
31127
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII71980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
151806
72.0%
013430
 
18.7%
25617
 
7.8%
31127
 
1.6%

status_max_archived_0_12_months
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
1
36777 
0
17248 
2
14880 
3
 
3070
5
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters71980
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
136777
51.1%
017248
24.0%
214880
20.7%
33070
 
4.3%
55
 
< 0.1%

Length

2021-05-19T00:29:49.367710image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-19T00:29:49.441346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
136777
51.1%
017248
24.0%
214880
20.7%
33070
 
4.3%
55
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
136777
51.1%
017248
24.0%
214880
20.7%
33070
 
4.3%
55
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number71980
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
136777
51.1%
017248
24.0%
214880
20.7%
33070
 
4.3%
55
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common71980
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
136777
51.1%
017248
24.0%
214880
20.7%
33070
 
4.3%
55
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII71980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
136777
51.1%
017248
24.0%
214880
20.7%
33070
 
4.3%
55
 
< 0.1%

status_max_archived_0_24_months
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
1
33955 
2
19465 
0
13430 
3
5117 
5
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters71980
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
133955
47.2%
219465
27.0%
013430
 
18.7%
35117
 
7.1%
513
 
< 0.1%

Length

2021-05-19T00:29:49.625294image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-19T00:29:49.698316image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
133955
47.2%
219465
27.0%
013430
 
18.7%
35117
 
7.1%
513
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
133955
47.2%
219465
27.0%
013430
 
18.7%
35117
 
7.1%
513
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number71980
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
133955
47.2%
219465
27.0%
013430
 
18.7%
35117
 
7.1%
513
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common71980
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
133955
47.2%
219465
27.0%
013430
 
18.7%
35117
 
7.1%
513
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII71980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
133955
47.2%
219465
27.0%
013430
 
18.7%
35117
 
7.1%
513
 
< 0.1%

status_max_archived_0_6_months
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
1
35710 
0
25858 
2
9140 
3
 
1272

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters71980
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
135710
49.6%
025858
35.9%
29140
 
12.7%
31272
 
1.8%

Length

2021-05-19T00:29:49.887830image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-19T00:29:49.953870image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
135710
49.6%
025858
35.9%
29140
 
12.7%
31272
 
1.8%

Most occurring characters

ValueCountFrequency (%)
135710
49.6%
025858
35.9%
29140
 
12.7%
31272
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number71980
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
135710
49.6%
025858
35.9%
29140
 
12.7%
31272
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common71980
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
135710
49.6%
025858
35.9%
29140
 
12.7%
31272
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII71980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
135710
49.6%
025858
35.9%
29140
 
12.7%
31272
 
1.8%

sum_capital_paid_account_0_12m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct17724
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10876.88586
Minimum0
Maximum571475
Zeros47492
Zeros (%)66.0%
Negative0
Negative (%)0.0%
Memory size562.5 KiB
2021-05-19T00:29:50.042241image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39046.75
95-th percentile58872.05
Maximum571475
Range571475
Interquartile range (IQR)9046.75

Descriptive statistics

Standard deviation26529.48399
Coefficient of variation (CV)2.439069816
Kurtosis38.17626435
Mean10876.88586
Median Absolute Deviation (MAD)0
Skewness4.833067879
Sum782918244
Variance703813520.6
MonotonicityNot monotonic
2021-05-19T00:29:50.159792image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
047492
66.0%
30033
 
< 0.1%
299028
 
< 0.1%
70019
 
< 0.1%
338518
 
< 0.1%
3106717
 
< 0.1%
264116
 
< 0.1%
690015
 
< 0.1%
4608415
 
< 0.1%
598015
 
< 0.1%
Other values (17714)24312
33.8%
ValueCountFrequency (%)
047492
66.0%
13
 
< 0.1%
22
 
< 0.1%
313
 
< 0.1%
410
 
< 0.1%
53
 
< 0.1%
61
 
< 0.1%
72
 
< 0.1%
91
 
< 0.1%
103
 
< 0.1%
ValueCountFrequency (%)
5714751
 
< 0.1%
4906722
< 0.1%
4527151
 
< 0.1%
4476781
 
< 0.1%
4132204
< 0.1%
3918361
 
< 0.1%
3917071
 
< 0.1%
3692201
 
< 0.1%
3584912
< 0.1%
3459971
 
< 0.1%

sum_capital_paid_account_12_24m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct13086
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6582.970603
Minimum0
Maximum341859
Zeros53768
Zeros (%)74.7%
Negative0
Negative (%)0.0%
Memory size562.5 KiB
2021-05-19T00:29:50.288240image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3115
95-th percentile39395
Maximum341859
Range341859
Interquartile range (IQR)115

Descriptive statistics

Standard deviation19176.77699
Coefficient of variation (CV)2.913088657
Kurtosis44.77826721
Mean6582.970603
Median Absolute Deviation (MAD)0
Skewness5.457809157
Sum473842224
Variance367748775.8
MonotonicityNot monotonic
2021-05-19T00:29:50.415386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
053768
74.7%
30053
 
0.1%
9697422
 
< 0.1%
348521
 
< 0.1%
2039019
 
< 0.1%
219019
 
< 0.1%
599018
 
< 0.1%
29516
 
< 0.1%
89515
 
< 0.1%
5015
 
< 0.1%
Other values (13076)18014
 
25.0%
ValueCountFrequency (%)
053768
74.7%
16
 
< 0.1%
23
 
< 0.1%
34
 
< 0.1%
42
 
< 0.1%
52
 
< 0.1%
63
 
< 0.1%
71
 
< 0.1%
91
 
< 0.1%
102
 
< 0.1%
ValueCountFrequency (%)
3418591
< 0.1%
3365682
< 0.1%
3339001
< 0.1%
3211021
< 0.1%
3158381
< 0.1%
3147381
< 0.1%
3135022
< 0.1%
3106821
< 0.1%
3079751
< 0.1%
3007041
< 0.1%

sum_paid_inv_0_12m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct31150
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39374.72313
Minimum0
Maximum2962870
Zeros15535
Zeros (%)21.6%
Negative0
Negative (%)0.0%
Memory size562.5 KiB
2021-05-19T00:29:50.550114image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12685
median16115
Q344050.5
95-th percentile151416.35
Maximum2962870
Range2962870
Interquartile range (IQR)41365.5

Descriptive statistics

Standard deviation89753.62445
Coefficient of variation (CV)2.279473157
Kurtosis407.2223734
Mean39374.72313
Median Absolute Deviation (MAD)16115
Skewness15.27146964
Sum2834192571
Variance8055713101
MonotonicityNot monotonic
2021-05-19T00:29:50.672083image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
015535
 
21.6%
895196
 
0.3%
1790113
 
0.2%
100072
 
0.1%
200071
 
0.1%
329059
 
0.1%
229058
 
0.1%
199057
 
0.1%
129057
 
0.1%
249057
 
0.1%
Other values (31140)55705
77.4%
ValueCountFrequency (%)
015535
21.6%
901
 
< 0.1%
1751
 
< 0.1%
2101
 
< 0.1%
2702
 
< 0.1%
2901
 
< 0.1%
2952
 
< 0.1%
3003
 
< 0.1%
3201
 
< 0.1%
3602
 
< 0.1%
ValueCountFrequency (%)
29628701
 
< 0.1%
28539921
 
< 0.1%
28356521
 
< 0.1%
27926941
 
< 0.1%
27892042
< 0.1%
27688352
< 0.1%
27468893
< 0.1%
27443621
 
< 0.1%
27254051
 
< 0.1%
27199171
 
< 0.1%

time_hours
Real number (ℝ≥0)

Distinct43033
Distinct (%)59.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.33900996
Minimum0.0002777777778
Maximum23.99972222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size562.5 KiB
2021-05-19T00:29:50.800304image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.0002777777778
5-th percentile7.364694444
Q111.62270833
median15.80930556
Q319.56083333
95-th percentile22.343625
Maximum23.99972222
Range23.99944444
Interquartile range (IQR)7.938125

Descriptive statistics

Standard deviation5.036018636
Coefficient of variation (CV)0.328314451
Kurtosis-0.2198772903
Mean15.33900996
Median Absolute Deviation (MAD)3.937638889
Skewness-0.5029547006
Sum1104101.937
Variance25.3614837
MonotonicityNot monotonic
2021-05-19T00:29:50.930863image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.2458
 
< 0.1%
21.484166678
 
< 0.1%
13.216666678
 
< 0.1%
11.693333337
 
< 0.1%
20.882222227
 
< 0.1%
18.176111117
 
< 0.1%
21.69257
 
< 0.1%
19.747777787
 
< 0.1%
19.295277787
 
< 0.1%
20.275277787
 
< 0.1%
Other values (43023)71907
99.9%
ValueCountFrequency (%)
0.00027777777781
< 0.1%
0.0016666666672
< 0.1%
0.0044444444441
< 0.1%
0.0055555555561
< 0.1%
0.0063888888891
< 0.1%
0.0072222222222
< 0.1%
0.0077777777781
< 0.1%
0.0080555555561
< 0.1%
0.0097222222221
< 0.1%
0.011388888891
< 0.1%
ValueCountFrequency (%)
23.999722221
 
< 0.1%
23.998333331
 
< 0.1%
23.996388893
< 0.1%
23.995833331
 
< 0.1%
23.994444441
 
< 0.1%
23.993055562
< 0.1%
23.992777781
 
< 0.1%
23.991666671
 
< 0.1%
23.991388891
 
< 0.1%
23.990277781
 
< 0.1%

uuid
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct71980
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.4 MiB
b1f51df9-76e6-44ac-92b2-c8d3d0554806
 
1
44c099b7-2ddc-4bd9-ad76-986c50ff98bc
 
1
4872a2b2-e133-4900-b4a9-e5d415fa70d3
 
1
5667309f-9c62-41c6-959b-e6c70f0cfa2f
 
1
e5f8b3b7-2dd9-4197-8eb8-ff4bdc40cc6b
 
1
Other values (71975)
71975 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters2591280
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71980 ?
Unique (%)100.0%

Sample

1st row082ef392-fa6f-4373-a2b6-c7deb9ef7090
2nd row91d4e7bd-5faf-4e8f-ad45-0c7146525768
3rd row927987c6-789e-4cf5-b24f-7e9634535576
4th rowbc1b6496-db68-4ae0-97a4-a3f08f2e6e9b
5th row3d9fc921-2aae-4990-ac26-230597ce4039

Common Values

ValueCountFrequency (%)
b1f51df9-76e6-44ac-92b2-c8d3d05548061
 
< 0.1%
44c099b7-2ddc-4bd9-ad76-986c50ff98bc1
 
< 0.1%
4872a2b2-e133-4900-b4a9-e5d415fa70d31
 
< 0.1%
5667309f-9c62-41c6-959b-e6c70f0cfa2f1
 
< 0.1%
e5f8b3b7-2dd9-4197-8eb8-ff4bdc40cc6b1
 
< 0.1%
f3335bb4-756e-49b9-9b8d-3d59a33431c71
 
< 0.1%
4f3e8faa-0039-41d2-9fff-eb1fafeb7aa81
 
< 0.1%
1c3a3d68-0efc-40d4-a5d5-e42965ef68bb1
 
< 0.1%
4cc9674d-62f7-4c20-9d50-50ddd8ff06f11
 
< 0.1%
a2b78055-56f6-4202-a40c-44a4fdd858191
 
< 0.1%
Other values (71970)71970
> 99.9%

Length

2021-05-19T00:29:51.174416image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b1f51df9-76e6-44ac-92b2-c8d3d05548061
 
< 0.1%
44c099b7-2ddc-4bd9-ad76-986c50ff98bc1
 
< 0.1%
4872a2b2-e133-4900-b4a9-e5d415fa70d31
 
< 0.1%
5667309f-9c62-41c6-959b-e6c70f0cfa2f1
 
< 0.1%
e5f8b3b7-2dd9-4197-8eb8-ff4bdc40cc6b1
 
< 0.1%
f3335bb4-756e-49b9-9b8d-3d59a33431c71
 
< 0.1%
4f3e8faa-0039-41d2-9fff-eb1fafeb7aa81
 
< 0.1%
1c3a3d68-0efc-40d4-a5d5-e42965ef68bb1
 
< 0.1%
4cc9674d-62f7-4c20-9d50-50ddd8ff06f11
 
< 0.1%
a2b78055-56f6-4202-a40c-44a4fdd858191
 
< 0.1%
Other values (71970)71970
> 99.9%

Most occurring characters

ValueCountFrequency (%)
-287920
 
11.1%
4206842
 
8.0%
8153707
 
5.9%
b153023
 
5.9%
9152613
 
5.9%
a152609
 
5.9%
5135261
 
5.2%
7135217
 
5.2%
6135177
 
5.2%
f135171
 
5.2%
Other values (7)943740
36.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1458152
56.3%
Lowercase Letter845208
32.6%
Dash Punctuation287920
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4206842
14.2%
8153707
10.5%
9152613
10.5%
5135261
9.3%
7135217
9.3%
6135177
9.3%
0135151
9.3%
2135057
9.3%
3134699
9.2%
1134428
9.2%
Lowercase Letter
ValueCountFrequency (%)
b153023
18.1%
a152609
18.1%
f135171
16.0%
e135007
16.0%
c134786
15.9%
d134612
15.9%
Dash Punctuation
ValueCountFrequency (%)
-287920
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1746072
67.4%
Latin845208
32.6%

Most frequent character per script

Common
ValueCountFrequency (%)
-287920
16.5%
4206842
11.8%
8153707
8.8%
9152613
8.7%
5135261
7.7%
7135217
7.7%
6135177
7.7%
0135151
7.7%
2135057
7.7%
3134699
7.7%
Latin
ValueCountFrequency (%)
b153023
18.1%
a152609
18.1%
f135171
16.0%
e135007
16.0%
c134786
15.9%
d134612
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2591280
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-287920
 
11.1%
4206842
 
8.0%
8153707
 
5.9%
b153023
 
5.9%
9152613
 
5.9%
a152609
 
5.9%
5135261
 
5.2%
7135217
 
5.2%
6135177
 
5.2%
f135171
 
5.2%
Other values (7)943740
36.4%

worst_status_active_inv
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing49970
Missing (%)69.4%
Memory size3.2 MiB
1.0
19463 
2.0
2425 
3.0
 
122

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters66030
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.019463
 
27.0%
2.02425
 
3.4%
3.0122
 
0.2%
(Missing)49970
69.4%

Length

2021-05-19T00:29:51.376065image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-19T00:29:51.435002image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.019463
88.4%
2.02425
 
11.0%
3.0122
 
0.6%

Most occurring characters

ValueCountFrequency (%)
.22010
33.3%
022010
33.3%
119463
29.5%
22425
 
3.7%
3122
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number44020
66.7%
Other Punctuation22010
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
022010
50.0%
119463
44.2%
22425
 
5.5%
3122
 
0.3%
Other Punctuation
ValueCountFrequency (%)
.22010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common66030
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.22010
33.3%
022010
33.3%
119463
29.5%
22425
 
3.7%
3122
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII66030
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.22010
33.3%
022010
33.3%
119463
29.5%
22425
 
3.7%
3122
 
0.2%

Interactions

2021-05-19T00:28:28.026050image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:28.145517image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:28.261930image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:28.374878image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:28.479360image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:28.595741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:28.701983image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:28.815274image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:28.927478image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:29.042008image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:29.153229image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:29.262244image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:29.371928image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:29.488595image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:29.604082image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:29.863686image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:29.972348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:30.077828image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:30.188668image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:30.313393image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:30.417543image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:30.530914image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:30.645781image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:30.756624image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:30.861335image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:30.972611image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:31.087810image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:31.201174image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:31.309716image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:31.436484image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:31.544275image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:31.652132image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:31.767421image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:31.888102image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:32.006487image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:32.119861image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:32.239349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:32.362951image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:32.482096image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:32.601254image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:32.721872image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:32.840904image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:32.965549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:33.095127image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:33.215134image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:33.340173image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:33.470067image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:33.598247image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:33.717775image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:33.842833image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:33.969090image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:34.276974image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:34.391752image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:34.516177image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:34.627754image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:34.742049image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:34.856304image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:34.974559image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:35.089786image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:35.203351image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:35.320941image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:35.441637image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:35.557800image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:35.675675image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:35.799666image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:35.917598image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:36.041493image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:36.169697image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:36.288987image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:36.413939image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:36.538606image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:36.657114image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:36.776680image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:36.891094image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:37.017851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:37.134565image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:37.254038image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:37.379660image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:37.493165image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:37.599344image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:37.707568image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:37.819280image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:37.935277image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:38.049285image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:38.164753image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:38.284462image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:38.397552image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:38.510322image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:38.622887image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:38.741514image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:38.866525image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:38.987592image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:39.089839image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:39.198757image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:39.312038image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:39.631181image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:39.736950image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:39.861976image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:39.988518image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:40.113132image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:40.228766image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:40.359951image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:40.477164image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:40.593685image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:40.711689image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:40.827737image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:40.942668image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:41.058301image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:41.177347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:41.305839image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:41.424483image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:41.555665image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:41.668468image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:41.781720image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:41.897835image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:42.029628image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:42.149541image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:42.270078image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:42.386853image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:42.504833image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:42.620242image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:42.723853image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:42.828390image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:42.933093image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:43.028999image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:43.145939image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:43.244755image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:43.357955image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:43.467475image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:43.571889image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:43.676686image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:43.801812image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:43.918456image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:44.036468image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:44.161577image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:44.275059image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:44.379733image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:44.481713image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:44.588070image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:44.690858image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:44.790267image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:44.896414image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:45.005163image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:28:45.117373image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2021-05-19T00:29:33.444394image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:33.553148image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:33.670648image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:33.781829image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:33.895577image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:34.012087image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:34.131255image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:34.253671image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:34.368977image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:34.479673image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:34.590050image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:34.706326image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:34.810051image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:34.924335image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:35.023229image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:35.128977image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:35.248051image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:35.358036image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:35.466475image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:35.577008image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:35.693312image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:35.811087image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:35.927862image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:36.042855image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:36.159401image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:36.268733image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:36.379819image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:36.494400image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:36.602465image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:36.717526image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:36.833799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-19T00:29:36.947151image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-05-19T00:29:51.573631image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-19T00:29:52.005996image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-19T00:29:52.481699image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-19T00:29:52.966872image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-19T00:29:53.411675image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-19T00:29:37.335239image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-19T00:29:38.715697image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-05-19T00:29:39.691691image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-05-19T00:29:40.113662image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

row_iduuidaccount_amount_added_12_24maccount_days_in_dc_12_24maccount_days_in_rem_12_24maccount_days_in_term_12_24maccount_incoming_debt_vs_paid_0_24maccount_statusaccount_worst_status_0_3maccount_worst_status_12_24maccount_worst_status_3_6maccount_worst_status_6_12mageavg_payment_span_0_12mavg_payment_span_0_3mmerchant_categorymerchant_grouphas_paidmax_paid_inv_0_12mmax_paid_inv_0_24mname_in_emailnum_active_div_by_paid_inv_0_12mnum_active_invnum_arch_dc_0_12mnum_arch_dc_12_24mnum_arch_ok_0_12mnum_arch_ok_12_24mnum_arch_rem_0_12mnum_arch_written_off_0_12mnum_arch_written_off_12_24mnum_unpaid_billsstatus_last_archived_0_24mstatus_2nd_last_archived_0_24mstatus_3rd_last_archived_0_24mstatus_max_archived_0_6_monthsstatus_max_archived_0_12_monthsstatus_max_archived_0_24_monthsrecovery_debtsum_capital_paid_account_0_12msum_capital_paid_account_12_24msum_paid_inv_0_12mtime_hoursworst_status_active_invdefault
027330082ef392-fa6f-4373-a2b6-c7deb9ef709000.00.00.00.0000001.01.01.01.01.04112.5454556.000000Diversified entertainmentEntertainmentTrue28367.028367.0L1+F0.000000000111400.00.0011111101189509804511.871944NaN0.0
13679291d4e7bd-5faf-4e8f-ad45-0c714652576800.00.00.0NaNNaNNaNNaNNaNNaN5626.000000NaNYouthful Shoes & ClothingClothing & ShoesTrue2190.02190.0F1+L0.0000000001000.00.00100011000219015.135000NaN0.0
241325927987c6-789e-4cf5-b24f-7e96345355760NaNNaNNaNNaNNaNNaNNaNNaNNaN3110.96774212.571429Diversified entertainmentEntertainmentTrue2993.04570.0no_match0.031250100302300.00.011111110004652519.8875001.00.0
374231bc1b6496-db68-4ae0-97a4-a3f08f2e6e9b0NaNNaNNaNNaNNaNNaNNaNNaNNaN447.000000NaNDiversified entertainmentEntertainmentTrue4290.016585.0F+L0.00000000031000.00.00111011000547520.678611NaN0.0
4290313d9fc921-2aae-4990-ac26-230597ce4039430240.00.00.02.1733181.01.01.01.01.03315.00000013.500000Sports gear & OutdoorLeisure, Sport & HobbyTrue25295.025295.0no_match0.0000000003100.00.0111111104209004052019.280278NaN0.0
534797a0bd962-772e-45b6-9e4f-3bb56f6fb6a2399340.087.00.00.0240731.02.02.01.02.0323.538462NaNDiversified entertainmentEntertainmentTrue2175.02175.0F+L0.23076930012010.00.061111220116603166051538520.4600001.00.0
6604967206cb77-04c4-449b-a8a8-de170e322b4700.00.00.00.0000001.01.0NaN1.0NaN5017.666667NaNConcept stores & MiscellaneousLeisure, Sport & HobbyTrue8280.08280.0F+L0.0000000003100.00.001111110478001990514.513056NaN0.0
772159b87306a-9a73-4198-b8a9-5e90c59b7daf00.00.00.0NaN1.01.0NaN1.01.0315.1176478.000000Diversified entertainmentEntertainmentTrue23400.023400.0F+L0.000000000171500.00.011111110007447621.802778NaN0.0
8700255c88cce8-ab92-45fb-8333-49a79c5c032d00.00.00.0NaNNaNNaNNaNNaNNaN26NaNNaNDiversified entertainmentEntertainmentTrue2385.02385.0no_match0.0000000000100.00.00100001000238510.966389NaN0.0
942090de12c788-814c-4fbb-a178-865e7b42ff32223600.00.00.00.5174921.02.01.02.01.02422.83333327.000000Diversified entertainmentEntertainmentTrue7760.07760.0F+L0.0000000005110.00.0921122206580682852127421.142778NaN0.0

Last rows

row_iduuidaccount_amount_added_12_24maccount_days_in_dc_12_24maccount_days_in_rem_12_24maccount_days_in_term_12_24maccount_incoming_debt_vs_paid_0_24maccount_statusaccount_worst_status_0_3maccount_worst_status_12_24maccount_worst_status_3_6maccount_worst_status_6_12mageavg_payment_span_0_12mavg_payment_span_0_3mmerchant_categorymerchant_grouphas_paidmax_paid_inv_0_12mmax_paid_inv_0_24mname_in_emailnum_active_div_by_paid_inv_0_12mnum_active_invnum_arch_dc_0_12mnum_arch_dc_12_24mnum_arch_ok_0_12mnum_arch_ok_12_24mnum_arch_rem_0_12mnum_arch_written_off_0_12mnum_arch_written_off_12_24mnum_unpaid_billsstatus_last_archived_0_24mstatus_2nd_last_archived_0_24mstatus_3rd_last_archived_0_24mstatus_max_archived_0_6_monthsstatus_max_archived_0_12_monthsstatus_max_archived_0_24_monthsrecovery_debtsum_capital_paid_account_0_12msum_capital_paid_account_12_24msum_paid_inv_0_12mtime_hoursworst_status_active_invdefault
71970391119d7fd49a-7641-4d55-9006-1cf6fd82c6f800.00.00.0NaNNaNNaNNaNNaNNaN2322.142857NaNBody & Hair CareHealth & BeautyTrue13680.013680.0no_match0.0000000005020.00.001212220004907910.858056NaN0.0
719716141388f5fffa-38e0-4c97-9238-a0f4e1dd10cf00.00.00.00.3727671.03.01.03.01.04030.500000NaNDiversified entertainmentEntertainmentTrue7195.016559.0F+L0.1000001014940.00.0511212302227703100520.8169441.00.0
7197281884a78c1147-f83e-4275-ac0d-15b20be8dc9300.00.00.0NaNNaNNaNNaNNaNNaN1814.500000NaNDiversified entertainmentEntertainmentTrue7495.07495.0F+L0.0000000004200.00.001111110001916020.896111NaN0.0
719732339480909c5e-b617-414c-a91e-a7877af137d700.00.00.01.8434671.01.0NaN1.01.026NaNNaNCosmeticsHealth & BeautyTrue0.00.0FNaN000000NaNNaN3000000042180014.463056NaN0.0
71974734824c22a9da-a68c-45a3-9253-2dc919e7f1f6415180.00.00.05.5321441.01.01.01.01.02138.07692319.5Youthful Shoes & ClothingClothing & ShoesTrue18772.018772.0F+L0.15384620051080.00.051122220462901129847.1297221.00.0
71975354870a9a2584-34d5-4824-ab9a-7fdc080fba0b00.00.00.0NaNNaNNaNNaNNaNNaN1814.00000014.0Youthful Shoes & ClothingClothing & ShoesTrue5085.05085.0F+L0.0000000001000.00.00100111000508518.189722NaN0.0
71976887268744e9ee-7971-4958-b8de-113449bd98b858250.00.00.01.4210251.01.01.01.02.02918.000000NaNDiversified Health & Beauty productsHealth & BeautyTrue12574.012574.0L1+F0.0000000001000.00.021001110999301257419.445000NaN0.0
7197742190cef72a33-9343-45cd-bc72-29db36323e6326070.00.00.01.0603061.02.01.02.02.0287.000000NaNBooks & MagazinesEntertainmentTrue12005.012005.0F0.0000000001000.00.0310001102121325402094020.188333NaN0.0
719785434795ce72b5-4787-4c5b-98c0-1094b1e7a5a000.00.00.0NaNNaNNaNNaNNaNNaN5022.11111118.5Books & MagazinesEntertainmentTrue2785.03885.0F0.1000001008610.00.011112220001303521.2822221.00.0
719791686671abfd91-ac64-4938-b8f5-dcfa7a2106580NaNNaNNaNNaNNaNNaNNaNNaNNaN3419.000000NaNGeneral Shoes & ClothingClothing & ShoesTrue4950.04950.0F+L0.0000000001000.00.00100011000495017.812500NaN0.0